library(haemdata)
use_pinboard("devel")
all_mice <- get_pin("mmu_mrna_all_mice_GENCODEm28_HLT_qc.rds")AML datasets
Note: Validation mice also available via Haemdata/Ondrive. See X for more details.
First, we need to setup the pin-board and download the mouse datasets.
se <- subset(all_mice,
select = grepl("2016", SummarizedExperiment::colData(all_mice)$batch)
)
pca <- pca_se(se)pca$biplotpca$pairs_plotpca$correlation_plotplot_transgenes_se(se)metadata_mmu |>
plot_cohort_survival("AML.mRNA.2016")se <- subset(all_mice,
select = grepl("AML.mRNA.2018.all_samples", SummarizedExperiment::colData(all_mice)$cohort)
)
pca <- pca_se(se)pca$biplotpca$pairs_plotpca$correlation_plotplot_transgenes_se(se)metadata_mmu |>
plot_cohort_survival("AML.mRNA.2018")se <- subset(all_mice,
select = grepl("AML.mRNA.2020", SummarizedExperiment::colData(all_mice)$cohort)
)
se <- subset(se,
select = grepl("PBMC", SummarizedExperiment::colData(se)$tissue)
)
pca <- pca_se(se)pca$biplotpca$pairs_plotpca$correlation_plotplot_transgenes_se(se)metadata_mmu |>
plot_cohort_survival("AML.mRNA.2020")se <- subset(all_mice,
select = grepl("RxGroup", SummarizedExperiment::colData(all_mice)$cohort)
)
pca <- pca_se(se)pca$biplotpca$pairs_plotpca$correlation_plotplot_transgenes_se(se)metadata_mmu |>
plot_cohort_survival("RxGroup")se <- subset(all_mice,
select = grepl("AML.scRNAseq.2022", SummarizedExperiment::colData(all_mice)$cohort)
)
pca <- pca_se(se)pca$biplotpca$pairs_plotpca$correlation_plotplot_transgenes_se(se)se <- subset(all_mice,
select = ("BM" == SummarizedExperiment::colData(all_mice)$tissue)
)
se <- subset(se,
select = grepl("AML", SummarizedExperiment::colData(se)$cohort)
)
pca <- pca_se(se)pca$biplotpca$pairs_plotpca$correlation_plotplot_transgenes_se(se)First, we need to setup the pin-board from which well retrieve the human datasets
library(haemdata)
use_pinboard("devel")City of Hope Biobank FLT3 patients
Include some background info on these samples, IRB etc
library(haemdata)
use_pinboard("devel")
se <- get_pin("hsa_mrna_flt3_GENCODEr40_qc.rds")
# ## Add week_scaled column, where time is scaled for each patient
# SummarizedExperiment::colData(se) <- SummarizedExperiment::colData(se) |>
# janitor::remove_constant() |>
# dplyr::as_tibble() |>
# dplyr::group_by(patient_id) |>
# dplyr::mutate(weeks_scaled = as.vector(scale(weeks))) |>
# dplyr::ungroup() |>
# `rownames<-`(colnames(se)) |>
# S4Vectors::DataFrame()
#pca <- pca_se(se, col_by = "weeks_scaled")
pca <- pca_se(se, col_by = "patient_id")pca$biplotpca$pairs_plotpca$correlation_plotBioproject: PRJEB27973
se <- get_pin("hsa_mrna_kim_GENCODEr40_qc.rds")
pca <- pca_se(se, col_by = "timepoint")pca$biplotpca$pairs_plotpca$correlation_plot